def applyDeltaWeights(self, dWList, dBList, updateOnlyLast, batchSize):
if self.useRPROP:
for i in reversed(xrange(self.NumberOfLayers - 1)):
cp.rprop(self.Weights[i], dWList[i], self.DeltaWeightsOld[i],
self.WeightsLearnRate[i], self.cfg.finetune_cost)
cp.rprop(self.Bias[i], dBList[i], self.DeltaBiasOld[i],
self.BiasLearnRate[i], self.cfg.finetune_cost)
if updateOnlyLast: break
else:
for i in reversed(xrange(self.NumberOfLayers - 1)):
W, B = self.Weights[i], self.Bias[i]
dW, dWo = dWList[i], self.DeltaWeightsOld[i]
dB, dBo = dBList[i], self.DeltaBiasOld[i]
cp.apply_binary_functor(dW, dWo, cp.binary_functor.XPBY,
self.cfg.finetune_momentum)
cp.apply_binary_functor(dB, dBo, cp.binary_functor.XPBY,
self.cfg.finetune_momentum)
cp.learn_step_weight_decay(
W, dW, self.cfg.finetune_learnrate / batchSize,
self.cfg.finetune_cost)
cp.learn_step_weight_decay(
B, dB, self.cfg.finetune_learnrate / batchSize,
self.cfg.finetune_cost)
cp.copy(dWo, dW)
cp.copy(dBo, dB)
if updateOnlyLast: break
def setMiniBatch(self, mb, dst_layer):
self.sampleset_ = mb
self.sampleset = cp.dev_tensor_float_cm(
self.sampleset_.astype('float32').copy('F'))
if hasattr(self, "norm"):
self.norm(self.sampleset)
cp.copy(dst_layer, self.sampleset)
def delta_output(self, calculated, correct): derivative = cp.dev_tensor_float_cm([calculated.shape[0], correct.shape[1]]) h = cp.dev_tensor_float_cm(derivative.shape) cp.copy(derivative, calculated) cp.apply_scalar_functor(derivative, cp.scalar_functor.DSIGM) cp.copy(h, correct) cp.apply_binary_functor(h, calculated, cp.binary_functor.SUBTRACT) cp.apply_binary_functor(derivative, h, cp.binary_functor.MULT) h.dealloc() return derivative
def delta_output(self, calculated, correct):
derivative = cp.dev_tensor_float_cm(
[calculated.shape[0], correct.shape[1]])
h = cp.dev_tensor_float_cm(derivative.shape)
cp.copy(derivative, calculated)
cp.apply_scalar_functor(derivative, cp.scalar_functor.DSIGM)
cp.copy(h, correct)
cp.apply_binary_functor(h, calculated, cp.binary_functor.SUBTRACT)
cp.apply_binary_functor(derivative, h, cp.binary_functor.MULT)
h.dealloc()
return derivative
def fit(self, input_matrix, teacher_matrix, n_epochs=100, learnrate = 0.10):
"""
Function to train the network
@param input_matrix -- matrix consisting of input data
to the network.
@param teacher_matrix -- matrix consisting of labels
of input data.
@param n_epochs -- number of epochs the network
is to be trained.
"""
n_samples = input_matrix.shape[-1]
squared_errors = cp.dev_tensor_float_cm(self.neuron_layers[-1].deltas.shape)
for r in xrange(n_epochs):
print "Epoch ", r + 1, "/", n_epochs
mse = 0.0
ce = 0.0
for batch in xrange(n_samples / self.batch_size):
index_begin = self.batch_size * batch
index_end = self.batch_size + index_begin
# Push input and teacher to GPU memory
# .copy("F") is needed since memory is non-contiguous
self.neuron_layers[0].activations = cp.dev_tensor_float_cm(
input_matrix[:, index_begin:index_end].copy('F'))
teacher_batch_host = teacher_matrix[:, index_begin:index_end]
teacher_batch = cp.dev_tensor_float_cm(teacher_batch_host.copy('F'))
# Forward-Pass
for i in xrange(self.n_layers):
self.weight_layers[i].forward()
# calculate error at output layer
cp.copy(self.neuron_layers[-1].deltas, teacher_batch)
self.neuron_layers[-1].deltas -= self.neuron_layers[-1].activations
cp.copy(squared_errors, self.neuron_layers[-1].deltas)
cp.apply_scalar_functor(squared_errors, cp.scalar_functor.SQUARE)
mse += cp.sum(squared_errors)
ce += float(np.sum(np.argmax(teacher_batch_host, axis=0)
!= np.argmax(self.neuron_layers[-1].activations.np, axis=0)))
# Backward-Pass
for i in xrange(self.n_layers):
self.weight_layers[self.n_layers - i - 1].backward(learnrate, decay=.01)
# Don't wait for garbage collector
teacher_batch.dealloc()
self.neuron_layers[0].activations.dealloc()
print "MSE: ", (mse / n_samples)
print "Classification Error Training: ", (ce / n_samples)
squared_errors.dealloc()
def applyDeltaWeights(self, dWList,dBList, updateOnlyLast, batchSize):
if self.useRPROP:
for i in reversed(xrange(self.NumberOfLayers-1)):
cp.rprop(self.Weights[i], dWList[i], self.DeltaWeightsOld[i], self.WeightsLearnRate[i], self.cfg.finetune_cost)
cp.rprop(self.Bias[i], dBList[i], self.DeltaBiasOld[i], self.BiasLearnRate[i], self.cfg.finetune_cost)
if updateOnlyLast: break
else:
for i in reversed(xrange(self.NumberOfLayers-1)):
W, B = self.Weights[i], self.Bias[i]
dW,dWo = dWList[i], self.DeltaWeightsOld[i]
dB,dBo = dBList[i], self.DeltaBiasOld[i]
cp.apply_binary_functor( dW, dWo, cp.binary_functor.XPBY, self.cfg.finetune_momentum)
cp.apply_binary_functor( dB, dBo, cp.binary_functor.XPBY, self.cfg.finetune_momentum)
cp.learn_step_weight_decay(W, dW, self.cfg.finetune_learnrate/batchSize, self.cfg.finetune_cost)
cp.learn_step_weight_decay(B, dB, self.cfg.finetune_learnrate/batchSize, self.cfg.finetune_cost)
cp.copy(dWo,dW)
cp.copy(dBo,dB)
if updateOnlyLast: break
def setMiniBatch(self, mb, dst_layer):
self.sampleset_ = mb
self.sampleset = cp.dev_tensor_float_cm(self.sampleset_.astype('float32').copy('F'))
if hasattr(self,"norm"):
self.norm(self.sampleset)
cp.copy(dst_layer,self.sampleset)
